Using neural networks to estimate motion vectors for motion corrected pet image reconstruction

Abstract

To reduce the effect(s) caused by patient breathing and movement during PET data acquisition, an unsupervised non-rigid image registration framework using deep learning is used to produce motion vectors for motion correction. In one embodiment, a differentiable spatial transformer layer is used to warp the moving image to the fixed image and use a stacked structure for deformation field refinement. Estimated deformation fields can be incorporated into an iterative image reconstruction process to perform motion compensated PET image reconstruction. The described method and system, using simulation and clinical data, provide reduced error compared to at least one iterative image registration process.

What is claimed is: 
     
         1 . A method of generating a motion compensation system comprising:
 obtaining a series of images including movement of at least one object between the series of images; and   training a machine learning-based system based on the series of images to produce a trained machine learning-based system for providing at least one motion vector indicating a movement of the at least one object between the series of images.   
     
     
         2 . The method as claimed in  claim 1 , wherein the training comprises minimizing a penalized loss function based on a similarity metric. 
     
     
         3 . The method as claimed in  claim 2 , wherein the similarity metric comprises a cross correlation function for correlating plural images of the series of images. 
     
     
         4 . The method as claimed in  claim 1 , wherein the series of images comprises a moving image and a fixed image, and
 wherein the training comprises warping the moving image to the fixed image using a differentiable spatial transform.   
     
     
         5 . The method as claimed in  claim 1 , wherein the machine learning-based system comprises a neural network and the trained machine learning-based system comprises a trained neural network. 
     
     
         6 . The method as claimed in  claim 1 , wherein the machine learning-based system comprises a neural network and the trained machine learning-based system comprises a trained neural network, and
 wherein the trained neural network comprises the neural network trained using unsupervised training.   
     
     
         7 . The method as claimed in any  claim 1 , wherein the machine learning-based system is trained using PET data. 
     
     
         8 . The method as claimed in  claim 1 , where in the machine learning-based system is trained using gated PET data. 
     
     
         9 . A trained machine learning-based system produced according to the method of  claim 1 . 
     
     
         10 . A system for generating a motion compensation system comprising:
 processing circuitry configured to:
 obtain a series of images including movement of at least one object between the series of images; and 
 train a machine learning-based system based on the series of images to produce a trained machine learning-based system for providing at least one motion vector indicating a movement of the at least one object between the series of images. 
   
     
     
         11 . The system as claimed in  claim 10 , wherein the processing circuitry configured to train comprises processing circuitry configured to minimize a penalized loss function based on a similarity metric. 
     
     
         12 . The system as claimed in  claim 11 , wherein the similarity metric comprises a cross correlation function for correlating plural images of the series of images. 
     
     
         13 . The system as claimed in  claim 10 , wherein the series of images comprises a moving image and a fixed image, and
 wherein the processing circuitry configured to train comprises processing circuitry configured to warp the moving image to the fixed image using a differentiable spatial transform.   
     
     
         14 . The system as claim in  claim 10 , wherein the machine learning-based system comprises a neural network and the trained machine learning-based system comprises a trained neural network. 
     
     
         15 . The system as claimed in  claim 10 , wherein the machine learning-based system comprises a neural network and the trained machine learning-based system comprises a trained neural network, and
 wherein the trained neural network comprises the neural network trained using unsupervised training.   
     
     
         16 . The system as claimed in  claim 10 , wherein the machine learning-based system is trained using PET data. 
     
     
         17 . The system as claimed  claim 10 , wherein the machine learning-based system is trained using gated PET data.